A Numerical Aggregation Algorithm for the Enzyme-Catalyzed Substrate Conversion
A Numerical Aggregation Algorithm for the Enzyme-Catalyzed Substrate Conversion
复制标题
酶催化底物转化的数值聚合算法
DOI：
10.1007/11885191_21
复制
发表时间：
2006
影响因子：
7.7
通讯作者：
V. Wolf
中科院分区：
文献类型：
--
作者：
H. Busch;W. Sandmann;V. Wolf
生物化学系统的计算模型通常非常大，而且如果不同反应类型的反应频率在数量级上存在差异，则模型具有刚度的数学性质，这使得传统方法很难甚至不可能分析系统。近年来，一种基于系统划分的加速随机模拟技术——慢尺度随机模拟算法被应用于酶催化的底物转化，以克服标准随机模拟在存在刚度时效率低下的问题。我们提出了一种基于类似划分的数值算法，但没有诉诸模拟。该算法利用与连续时间马尔可夫链的连接，并将整个问题分解为更小的易处理的子问题。数值结果表明，相对于加速随机模拟，效率有了很大的提高。
Computational models of biochemical systems are usually very large, and moreover, if reaction frequencies of different reaction types differ in orders of magnitude, models possess the mathematical property of stiffness, which renders system analysis difficult and often even impossible with traditional methods. Recently, an accelerated stochastic simulation technique based on a system partitioning, the slow-scale stochastic simulation algorithm, has been applied to the enzyme-catalyzed substrate conversion to circumvent the inefficiency of standard stochastic simulation in the presence of stiffness. We propose a numerical algorithm based on a similar partitioning but without resorting to simulation. The algorithm exploits the connection to continuous-time Markov chains and decomposes the overall problem to significantly smaller subproblems that become tractable. Numerical results show enormous efficiency improvements relative to accelerated stochastic simulation.